Loss Function
A function that measures how far model predictions are from actual values.
In-depth explanation
Loss functions quantify prediction error, guiding the optimization process. Different tasks use different loss functions: mean squared error (MSE) for regression, cross-entropy for classification, and specialized losses for ranking or detection. The choice of loss function significantly impacts what the model learns to optimize.
Examples
Related terms
More in Machine Learning
Supervised Learning
Machine learning approach where models learn from labeled training data to predict outcomes.
Unsupervised Learning
Machine learning approach where models find patterns in data without labeled examples.
Semi-Supervised Learning
Machine learning approach using a small amount of labeled data with a large amount of unlabeled data.
Classification
Predicting which category or class an input belongs to from a set of predefined categories.
Regression
Predicting a continuous numerical value based on input features.
Feature
An individual measurable property or characteristic of data used as input to a machine learning model.
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